In the realm of smart sensing with the Internet of Things, earable devices
are empowered with the capability of multi-modality sensing and intelligence of
context-aware computing, leading to its wide usage in Human Activity
Recognition (HAR). Nonetheless, unlike the movements captured by Inertial
Measurement Unit (IMU) sensors placed on the upper or lower body, those motion
signals obtained from earable devices show significant changes in amplitudes
and patterns, especially in the presence of dynamic and unpredictable head
movements, posing a significant challenge for activity classification. In this
work, we present EarDA, an adversarial-based domain adaptation system to
extract the domain-independent features across different sensor locations.
Moreover, while most deep learning methods commonly rely on training with
substantial amounts of labeled data to offer good accuracy, the proposed scheme
can release the potential usage of publicly available smartphone-based IMU
datasets. Furthermore, we explore the feasibility of applying a filter-based
data processing method to mitigate the impact of head movement. EarDA, the
proposed system, enables more data-efficient and accurate activity sensing. It
achieves an accuracy of 88.8% under HAR task, demonstrating a significant 43%
improvement over methods without domain adaptation. This clearly showcases its
effectiveness in mitigating domain gaps.

基于领域适应技术的 EarDA 系统，能够从不同传感器位置提取独立于领域的特征，同时采用基于过滤器的数据处理方法来减轻头部运动对活动识别的影响，实现更加高效准确的活动感知，优于无领域适应方法的 43%。